In order to take advantage of the higher temporal resolution of swath based sea ice products with accurate timestamps, Level 2 data will be provided to the model partners for assimilation and validation purposes. The actual production of the L2 data will be done in WPs 2-7. First test version at KO+12, and production version at KO+24.
An operational data product of sea ice thickness and freeboard from SAR altimetry data will be published. This data product will concentrate on regions of high interest, and it will provide highest possible spatial and temporal resolution.
Estimates of snow and ice parameters from snap shots or time series of NWP and satellite data. Snow/ice parameters should include snow thickness, snow density and snow/ice interface temperature. The dataset should include Arctic wide coverage for the month of May during several years. First version at KO+18, and final at KO+33.
Various existing algorithms for SAR based sea ice classification are further developed for dual-polarized Sentinel-1 and Radarsat-2 images. The methods include nonlinear clustering algorithms, multiple-polarization SAR segmentation algorithms, as well as segmentation and classification algorithms based on segment-wise features.
Time series of snow/ice parameters along buoy drift trajectories as ASCII files in ESA CCI RRDP format. Parameters include as many as possible of snow thickness, snow density, ice thickness, surface temperature, ice/snow interface temperature, temperatures at standard levels in snow and ice. First version at KO+9, 2nd version at KO+21, and final at KO+30.
Time series of snow/ice parameters along ice drift trajectories as ASCII files in ESA CCI RRDP format. Parameters include as many as possible of snow thickness, snow density, ice thickness, surface temperature, ice/snow interface temperature, temperatures at standard levels in snow and ice. First version at KO+9, and final at KO+21.
Description of methods and software for pre-processing of SAR, e.g. georectifcation, calibration, incidence angle scaling, filtering.
Production of albedo, MPF and ice concentration data sets, for at least three years, based on MERIS (2002-2012), AMSR-E and SMOS (starting on 2010) and starting on 2015 based on Sentinel-3 (optical) and AMSR2 and SMOS/SMAP observations.
The Microwave Imagine Radiometer with Aperture Synthesis (MIRAS) aboard ESA's SMOS satellite measures the Earth's surface brightness temperature (TB) at L-Band (frequency of 1.4 GHz). NASA's SMAP spacecraft carries a 1.2 GHz radar and a 1.4 GHz radiometer that share a single feedhorn and a mesh reflector. The synthetic aperture technique of SMOS allows to measure TB at a range of incidence angles while SMAP uses a conical scan geometry and a constant incidence angle at 40°. In order to generate a homogeneous SMOS/SMAP data product the SMOS TB will be interpolated to the SMAP incidence angle of 40°. SMOS and SMAP polarized TBs and their estimated uncertainties will be projected into a common grid, e.g. polar stereographic or EASE. Data products will be generated using standard NetCDF format.
A new data product will be generated from the CryoSat-2 data. Differently from usual monthly products, a weekly product will be generated, including the frequent updates from orbit data.
Set of SAR based sea ice products generated using the developed novel algorithms for utilization in other WPs. First version at KO+12, updated throughout to KO+18.
Optimal estimation inversion tool to compute estimations of snow and ice parameters from time series of NWP and satellite data. Snow/ice parameters should include snow thickness, snow density and snow/ice interface temperature. First version at KO+8, and final at KO+30.
NASA's SMAP 1.2 GHz radar measures the normalized backscatter coefficient sigma-0 at a high resolution (1-3 km) over the Arctic Ocean. A product of sigma-0 values and their uncertainties will be defined as a grid compatible to the TB grid, i.e. with the same projection as the SMOS/SMAP TB. Data products will be generated using standard NetCDF format.
Earlier the possibility of sea ice classification using Airborne Synthetic Aperture and Interferometric Radar Altimeter System (ASIRAS) was demonstrated. Significant differences between waveform shape parameters allowed to classify first-year-ice and multiyear ice as well as leads by applying a Bayesian based method. Further analyses are conducted to test how these results can be adapted to satellite borne altimeter systems.
This is a data product of sea ice type in digital format (netcdf following CF convention) based on radar altimeter data. The product will be made freely available for the scientific community.
Time series of satellite and ERA Interim NWP data colocated with the buoy and ice drift trajectories from D1.2 and D1.3. Satellite data should include as many as possible of AMSR, SMOS, ASCAT, IR, SMAP, OSCAT, SSMIS, Sentinel-1 and Cryosat. NWP data every 6 hours should include: air pressure [MSL; 151], 2 m air temperature [2T; 167], 10 m wind speed U [10U; 165], 10 m wind speed V [10V; 166], solar short wave incoming radiation [SSRD; 169], thermal longwave incoming radiation [STRD; 175], dewpoint-temp [2D; 168], Total precipitation (m) [TP; 228], TotalCloudLiquidWater [TCLW; 78], TotalCloudIceWater [TCIW; 79] and TotalCloudWaterVapour [TCWV; 137] First version at KO+12, 2nd at KO+24, and final at KO+36.
Description of methods for assimilating CryoSat-2 based estimations of e.g. sea ice typing and thickness along the satellite ground track (products from D7.1 ) into seasonal forecasting systems.
A demonstration of retrieving sea ice type from radar altimeter data and the documentation of the methodology used.
The operational SMOS algorithm of UHAM will be adjusted for the use with SMOS and SMAP TBs at a constant incidence angle. The ice thickness and its uncertainty will be estimated from the TBs and delivered on the common grid. Data products will be generated using standard NetCDF format.
An intermediate product of CryoSat-2 data processing is a sea-surface height product. This will be extracted and made publicly available for various external applications, e.g. in oceanography.
Determination of the albedo and MPF retrievals based on PM observations.
Source code and documentation of model to compute time series of expected satellite signatures along ice drift trajectories from WP 1. Signatures should include at least TBs at AMSR and SMOS wavelengths and backscatter at C- and Ku-band. First version at KO+12, and final at KO+24.
SPICES organizes thematic workshops for potential end-users, and participates in, both scientifical and industrial, workshops/meetings. Based on these SPICES Innovation Management and Service Plan is formed. First version at K0+12, updated at K0+24 and K0+36.
The quality of SMOS and SMAP TBs will be compared. Potential biases between the different sensor products will be analysed. The influence of error sources such as RFI will be investigated. The uncertainty of SMOS and SMAP TBs will be estimated from time series over stable targets.
This deliverable consists of a report that defines and evaluates a list of user-driven metrics that are useful for the evaluation of regional forecast performance. This implies also an evaluation of methods used for downscaling or upscaling of simulation output and the observational data sets.
Description of plans for management of Open Research Data (archiving, sharing, access, search, dissemination etc.) and data management within the SPICES project between the partners.
The deployments of various buoy types need to be coordinated among the project partners and within international networks. This coordination will mostly take place during the first three months and will be summarized in the report. However, this plan will be updated regularly over the entire project duration.
Describes limits of sea ice thickness determination from SMOS during the onset of melt.
Determination of the influence of MPF on sea ice concentration retrieval using albedo and MPF data from existing retrievals, in situ observation of melt ponds from Polarstern bridge observations and aerial images taken during EM Bird flights.
CryoSat-2 data products are usually released as gridded data, averaging over multiple orbits and thus averaging over time. Here we will assess the uncertainty of single orbit data though direct comparisons with field observations and in comparison with single orbits of the same region with minimal time offset.
SMOS and SMAP data products of sea ice thickness will be validated using airborne ice thickness measurements. The potential of SMAP polarized sigma-0 for surface classification and disaggregation will be evaluated. A strategy for the potential improvement of the ice thickness retrieval using a combination of active and passive L-band measurements will be described.
This deliverable consists of a report documenting the predictive skill (from weeks to months) of the sea-ice conditions and their impact of the atmosphere achieved by the ECMWF forecasting systems. The new observations and metrics resulting from SPICES will be used in the evaluation. The impact of selected SPICES-data sets on the initialization of sea-ice will be evaluated.
Overview of major SPICES results and description of developed end-user products.
Statistical relation between (albedo and MPF) and brightness temperatures of PM sensors from 1.4 to 89 GHz, with seasonal and regional dependences.
The procedure for the retrieval of sea-ice thickness will be applied to areas of frazil-pancake (FP) ice during periods of new ice formation and ice growth in regions of turbulence. Both ESA Sentinel-1 (S1) C-band and Cosmo-SkyMed (CSK) X-band SAR images, in areas of the Arctic (Greenland Sea) and of Antarctica (Ross Sea), will be used. A first phase of the study will focus on the development of a processing scheme for the automatic detection of FP ice fields and on the comparison of the results obtained with S1 and CSK images. By extracting a subset of the image across the ice edge, the SAR-wave spectra, both in ice and open sea, will be computed; these spectra will be used as input to a wave-ice interaction model to generate ice thicknesses. The results of this procedure will be validated with direct ice measurements performed during field campaigns carried out by other WPs of the project. The final deliverable will be seasonal pancake-frazil ice thicknesses (i.e. ice volume per unit sea surface area) and thus ice mass fluxes, for specific regions in which frazil-pancake ice is the dominant ice type. These will include: in the Antarctic the outer growing ice edge in early winter, and the Ross Sea and similar coastal polynyas throughout the year; in the Arctic the Odden ice tongue region and selected coastal polynyas in areas such as the Bering Sea coastline. In collaboration with UB, areas of thin ice will be selected where both SMOS and SAR imagery are available. UB will carry out SMOS retrievals yielding thickness values based on the SMOS algorithm, while UNIVPM and CNR will retrieve thicknesses using the pancake wave method. Results will be compared in an attempt to find a cross-correlation between SMOS and SAR in frazil-pancake ice regions.
Communication plans with potential end-users of the new products. Planning of two workshops for promoting new SPICES products and getting feedback and suggestions for improvement from the end-user community. SPICES publication plan (peer reviewed open access scientific publications, conferences and workshops). Outreach and promotion brochures addressed specifically to an end user group and to the wider scientific community. Key workshop forums that the SPICES project will commit to presenting its results and products. First version at KO+6, updated at KO+26.
Validation of SAR based sea ice products: input datasets, methods, results (e.g. relative and absolute accuracies).
SPICES results during the second year.
Comparison of the sea ice type classification results based on radar altimeter data and other sea ice type products, such as the OSI-SAF sea ice type product, available.
Transfer existing albedo and MPF retrieval algorithm based on MERIS to Sentinel-3, including cloud screening.
SPICES results during the first year.
Searching for OpenAIRE data...
Author(s): Marko Mäkynen; Juha Karvonen
Published in: Remote Sensing, Issue 9/12, 2017, Page(s) 1324, ISSN 2072-4292
Author(s): Alexandru Gegiuc, Markku Similä, Juha Karvonen, Mikko Lensu, Marko Mäkynen, Jouni Vainio
Published in: The Cryosphere, Issue 12/1, 2018, Page(s) 343-364, ISSN 1994-0424
Author(s): Robert Ricker, Stefan Hendricks, Fanny Girard-Ardhuin, Lars Kaleschke, Camille Lique, Xiangshan Tian-Kunze, Marcel Nicolaus, Thomas Krumpen
Published in: Geophysical Research Letters, Issue 44/7, 2017, Page(s) 3236-3245, ISSN 0094-8276
Author(s): Miguel Moctezuma-Flores, Flavio Parmiggiani, Corrado Fragiacomo, Lorenzo Guerrieri
Published in: Journal of Applied Remote Sensing, Issue 11/2, 2017, Page(s) 026041, ISSN 1931-3195
Author(s): A. Di Bella, H. Skourup, J. Bouffard, T. Parrinello
Published in: Advances in Space Research, 2018, ISSN 0273-1177
Author(s): P. Wadhams, G. Aulicino, F. Parmiggiani, P. O. G. Persson, B. Holt
Published in: Journal of Geophysical Research: Oceans, Issue 123/3, 2018, Page(s) 2213-2237, ISSN 2169-9275
Author(s): Peng Lu, Matti Leppäranta, Bin Cheng, Zhijun Li, Larysa Istomina, Georg Heygster
Published in: The Cryosphere, Issue 12/4, 2018, Page(s) 1331-1345, ISSN 1994-0424
Author(s): Marko Makynen, Juha Karvonen
Published in: IEEE Transactions on Geoscience and Remote Sensing, Issue 55/11, 2017, Page(s) 6170-6181, ISSN 0196-2892
Published in: ISSN 0143-1161
Author(s): Amelie Schmitt, Lars Kaleschke
Published in: Remote Sensing, Issue 10/4, 2018, Page(s) 553, ISSN 2072-4292
Author(s): Juha Karvonen
Published in: The Cryosphere, Issue 12/8, 2018, Page(s) 2595-2607, ISSN 1994-0424
Published in: ISSN 0143-1161
Author(s): Robert Ricker, Fanny Girard-Ardhuin, Thomas Krumpen, Camille Lique
Published in: The Cryosphere, Issue 12/9, 2018, Page(s) 3017-3032, ISSN 1994-0424
Author(s): Miguel Moctezuma-Flores, Fiorigi F. Parmiggiani, Lorenzo Guerrieri
Published in: Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2017, 2017, Page(s) 22